当前位置: X-MOL 学术J. Theor. Probab. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Convergence to Equilibrium for Time-Inhomogeneous Jump Diffusions with State-Dependent Jump Intensity
Journal of Theoretical Probability ( IF 0.8 ) Pub Date : 2019-09-30 , DOI: 10.1007/s10959-019-00947-4
E. Löcherbach

We consider a time-inhomogeneous Markov process $$X = (X_t)_t$$ X = ( X t ) t with jumps having state-dependent jump intensity, with values in $${\mathbb {R}}^d , $$ R d , and we are interested in its longtime behavior. The infinitesimal generator of the process is given for any sufficiently smooth test function f by $$\begin{aligned} L_t f (x) = \sum _{i=1}^d \frac{\partial f}{\partial x_i } (x) b^i ( t,x) + \int _{{\mathbb {R}}^m } [ f ( x + c ( t, z, x)) - f(x)] \gamma ( t, z, x) \mu (\mathrm{d}z ) , \end{aligned}$$ L t f ( x ) = ∑ i = 1 d ∂ f ∂ x i ( x ) b i ( t , x ) + ∫ R m [ f ( x + c ( t , z , x ) ) - f ( x ) ] γ ( t , z , x ) μ ( d z ) , where $$ \mu $$ μ is a $$\sigma $$ σ -finite measure on $$({\mathbb {R}}^m , {\mathcal B} ( {\mathbb {R}}^m ) ) $$ ( R m , B ( R m ) ) describing the jumps of the process. We give conditions on the coefficients b ( t , x ) , c ( t , z , x ) and $$ \gamma ( t, z, x ) $$ γ ( t , z , x ) under which the longtime behavior of X can be related to the longtime behavior of a time-homogeneous limit process $${\bar{X}} . $$ X ¯ . Moreover, we introduce a coupling method for the limit process which is entirely based on certain of its big jumps and which relies on the regeneration method. We state explicit conditions in terms of the coefficients of the process allowing control of the speed of convergence to equilibrium both for X and for $${\bar{X}}$$ X ¯ .

中文翻译:

具有状态依赖跳跃强度的时间非均匀跳跃扩散的收敛到平衡

我们考虑一个时间非齐次的马尔可夫过程 $$X = (X_t)_t$$ X = ( X t ) t 的跳跃具有状态依赖的跳跃强度,值在 $${\mathbb {R}}^d , $ $ R d ,我们对其长期行为感兴趣。对于任何足够平滑的测试函数 f,该过程的无穷小生成器由 $$\begin{aligned} L_t f (x) = \sum _{i=1}^d \frac{\partial f}{\partial x_i } (x) b^i ( t,x) + \int _{{\mathbb {R}}^m } [ f ( x + c ( t, z, x)) - f(x)] \gamma ( t, z, x) \mu (\mathrm{d}z ) , \end{aligned}$$ L tf ( x ) = ∑ i = 1 d ∂ f ∂ xi ( x ) bi ( t , x ) + ∫ R m [ f ( x + c ( t , z , x ) ) - f ( x ) ] γ ( t , z , x ) μ ( dz ) ,其中 $$ \mu $$ μ 是 $$\sigma $ $ σ - $$({\mathbb {R}}^m , {\mathcal B} ( {\mathbb {R}}^m ) ) $$ ( R m , B ( R m ) ) 上的有限度量,描述了过程的跳跃。我们给出了系数 b ( t , x ) , c ( t , z , x ) 和 $$ \gamma ( t, z, x ) $$ γ ( t , z , x ) 的条件,在这些条件下 X 的长期行为可能与时间齐次限制过程 $${\bar{X}} 的长期行为有关。$$ X¯ 。此外,我们为极限过程引入了一种耦合方法,该方法完全基于其某些大跳跃,并依赖于再生方法。我们根据过程的系数声明了明确的条件,允许控制 X 和 $${\bar{X}}$$ X¯ 的收敛速度达到平衡。我们为极限过程引入了一种耦合方法,该方法完全基于它的某些大跳跃,并依赖于再生方法。我们根据过程的系数陈述了明确的条件,允许控制 X 和 $${\bar{X}}$$ X¯ 的收敛速度达到平衡。我们为极限过程引入了一种耦合方法,该方法完全基于其某些大跳跃并依赖于再生方法。我们根据过程的系数声明了明确的条件,允许控制 X 和 $${\bar{X}}$$ X¯ 的收敛速度达到平衡。
更新日期:2019-09-30
down
wechat
bug